Data Cleaning Project for the Coursera "Getting and Cleaning Data" course

The dataset used is the UCI HAR Dataset

Notes on what the run_analysis.R script performs

1. Cleans up the environemnt (so be careful to save your environment before you run the script!)

2. Loads libraries and obtains the working directory. Assumption is the UCI HAR Dataset directory would be present in the working directory

3. Reads the data from the appropriate directories/files

   • Reference data
   • Test data and
   • Training data

4. Names/renames the columns (aka variables) using the Reference data

   NOTE: Merging the Test and Train datasets have been put off until reduced datasets containing only the variables of interest are created

5. Select the variables of interest ( all computed mean() and stdev() ) from the test and train datasets (we still have separate datasets for test and train)

6. Enhace the dataset obtained above with the subject and activity information from the y_ and subject_ files

   • for this step, activity numbers obtained from the 'y_' files have been reinterpreted utilizing the information obtained from the activitylabels.txt file (a new column was added to the 'y' data)

7. Merge the test and train datasets. We now get a dataset with rows equal to the sum of the number of rows in x_test and x_train. The resulting dataset has most column names obtained from the the reference.txt file

8. Variables of the dataset are labeled with descriptive variable names by interpreting the column names obtained from the the reference.txt file

9. A new dataset is created by computing the average of all variables by groups of

   • the subject (which remains a numeric identifier)
   • then by the activity label (6 types, obtained from the activitylabels.txt file)

   NOTE: This is the final tidy dataset

10. The data frame (containing the final data set) is written into the UCI_HAR.txt file

11. The environment is tidyed up post the script activities were performed